We develop deep clustering survival machines to simultaneously predict survival information and characterize data heterogeneity that is not typically modeled by conventional survival analysis methods. By modeling timing information of survival data generatively with a mixture of parametric distributions, referred to as expert distributions, our method learns weights of the expert distributions for individual instances based on their features discriminatively such that each instance's survival information can be characterized by a weighted combination of the learned expert distributions. Extensive experiments on both real and synthetic datasets have demonstrated that our method is capable of obtaining promising clustering results and competitive time-to-event predicting performance.